We first fix T to the commonly used 8 frames [1]}, [2]} and vary the temporal stride \(\tau \)\(\in \) {1,2,4,6,8,10} with results shown in Fig. REF  (a and c on a segment and sequence level respectively). It is clear that temporally aware TSM models outperform standard TSN models across all temporal strides, and maintain a high F1 score beyond \(\tau =2\) , while the performance of a regular TSN model, incapable of temporal learning, drops as \(\tau \)  increases, suggesting sparser frames can introduce more useful long term information for classification, but only when temporal learning is possible. This drop in performance is more significant on a sequence level with the performance of some classes dropping to an F1 score of 0 at \(\tau =10\) , suggesting sparser frames when temporal learning is not applicable leads to significant confusion when operating in a sliding window fashion.
